A18 INTERKINGDOM DYNAMICS AND NUTRITION ARE ASSOCIATED WITH DIVERGENT MATURATIONAL PATTERNS OF THE INFANT BACTERIAL AND FUNGAL GUT MICROBIOME
Notice bibliographique
Résumé
Abstract Background Early life has been identified as a critical window, during which time deviations from typical patterns of gut microbiome maturation have been associated with adverse health outcomes later in life. In the first 2-3 years of life, the infant gut microbiome undergoes ecological shifts characterized by increasing bacterial alpha diversity and variable changes in fungal alpha diversity. Research has shown not all infants follow these maturational trends, but our understandings of the factors linked to atypical microbiome maturation patterns are limited. Purpose We assessed bacterial and fungal gut microbiome maturation in early life to determine if atypical maturational patterns were observed in otherwise healthy infants and identify factors associated with these patterns. Method In 100 infants from the CHILD Cohort Study, we assessed the bacterial and fungal gut microbiome in stool samples collected at 3 and 12 months of age using 16S and ITS2 Illumina sequencing, respectively. We performed untargeted metabolomics on urine samples collected at 3 and 12 months using liquid chromatography-mass spectrometry/mass spectrometry. Microbiome and metabolomic measures were evaluated by ecological and multivariate analyses using RStudio. Result(s) Gut microbiome analyses revealed 24% and 20% of infants displayed atypical alpha diversity trajectories in the first year of life for bacteria or fungi, respectively. Atypical patterns were linked to reduced abundance of Bacteroides and increased Candida at 3 months. Functional analysis revealed an atypical bacterial alpha diversity trend was associated with elevated urinary trimethylamine N-oxide, creatine, indole acetic acid, and 2-furoylglycine, and an atypical fungal trend was associated with elevated urinary lactate. Using decision trees, the strongest predictors of atypical alpha diversity trends were interkingdom dynamics, breastfeeding duration, and maternal diet during pregnancy. Logistic regression revealed an atypical bacterial trend was positively associated with delivery via C-section and inversely associated with exclusive breastfeeding at 3 months, and an atypical fungal trend was positively associated with gestational consumption of artificially sweetened beverages and inversely associated with prenatal antibiotics. Interkingdom network analyses revealed the gut microbiome of infants with an atypical bacterial or fungal alpha diversity trend displayed a greater number of interkingdom interactions reflective of a less stable or immature gut microbiome. Conclusion(s) Our findings reveal a substantial proportion of infants display atypical patterns of gut microbiome maturation in the first year of life. While known microbiome-modifying factors were important determinants of maturational patterns, these factors were generally less influential than interkingdom influences. Together, this highlights the importance of interkingdom analyses at the individual level to generate more nuanced understandings of maturational trajectories in early life. Please acknowledge all funding agencies by checking the applicable boxes below CIHR, Other Please indicate your source of funding; Alberta Children's Hospital Research Institute Disclosure of Interest None Declared
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».